BACKGROUND: The COVID-19 pandemic has inflicted global devastation, infecting over 750 million and causing 6 million deaths. In an effort to control the spread of the virus, governments around the world implemented a variety of measures, including st...
The role of resources and capabilities in shaping and implementing a firm's strategy is paramount. The COVID-19 pandemic underscored the necessity for managers to possess a decision-making model that facilitates the selection of resources and capabil...
As we move beyond the COVID-19 pandemic, the risk of future infodemics remains significant, driven by emerging health crises and the increasing influence of artificial intelligence in the information ecosystem. During periods of apparent stability, p...
This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating epidemiological, mobility, vaccination, and environmental data ...
BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these risks post-pandemic have been absent. We aimed to use large electronic health records (EHR) and ML a...
BACKGROUND: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Di...
Changes in drug use in the general population during the COVID-19 pandemic and their long-term consequences are not well understood. We employed natural language processing and machine learning to analyse a large dataset of self-reported rates of and...
OBJECTIVE: Our study aims to enhance epidemic intelligence through event-based surveillance in an emerging pandemic context. We classified electronic health records (EHRs) from La Rioja, Argentina, focusing on predicting COVID-19-related categories i...
The landscape of diagnostic testing is undergoing a significant transformation, driven by the integration of artificial intelligence (AI) and machine learning (ML) into decentralized, rapid, and accessible sensor platforms for point-of-care testing (...
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